metadata
license: mit
language:
- en
library_name: jax
tags:
- perturbation-prediction
- prior-data-fitted-networks
- in-context-learning
- single-cell
- causal-inference
- diffusion-transformer
datasets:
- marvinsxtr/MapPFN
pipeline_tag: other
MapPFN Weights
Pre-trained and fine-tuned checkpoints for MapPFN: Learning Causal Perturbation Maps in Context (Sextro et al., 2026).
Checkpoints
model.ckpt— Pre-trained on synthetic biological prior (50 dimensions, 400k steps)model_finetuned_frangieh.ckpt— Fine-tuned on Frangieh et al. (2021)model_finetuned_papalexi.ckpt— Fine-tuned on Papalexi et al. (2021)
All checkpoints share the same MMDiT architecture (~25M parameters) and differ only in training data. See the GitHub repository for inference and fine-tuning code.
Citation
@inproceedings{sextro2026mappfn,
title = {Map{PFN}: Learning Causal Perturbation Maps in Context},
author = {Marvin Sextro and Weronika K{\l}os and Gabriel Dernbach},
booktitle = {Advances in Neural Information Processing Systems},
volume = {39},
year = {2026}
}
Links: Paper | Code | Datasets | Project Page